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Data anonymization can help Telcos to share data to facilitate open innovation. Two big challenges to address while anonymizing Telco data for AI usecases are (a) fool-proof against de-anonymization (b) Not hamper the power (Ex: predictive, Classification) of the AI models. The talk we cover the following:

• State of art of Data Anonymization applied to Telcos, including the research works, projects and specifications.
• What constitutes the sensitive data (names, addresses, telco-specific fields, location-data, etc). in Telco scenarios
• Anonymization categories such as (Suppression, Masking, Pseudonymization, Generalization, Swapping, Perturbation and Synthetic Data Generation).
• Approaches/techniques ranging from classic (ex: K-Anonymity) to use of NLP to GANs (Generative Adversarial Networks).
• Which of the above categories and techniques are applicable to Telco Data, considering the challenges of deanonymization and model-power.
• Demonstration of the developed unified-anonymization tool.

In this session, we will explore the transformative power of machine learning in optimizing sustainable energy production from agricultural waste. Specifically, we will examine how predictive analytics can be employed to forecast spatial biomass distribution, efficiently allocate resources, and design a cost-effective bioenergy supply chain. Using real-world examples, we’ll demonstrate how these technologies are vital in transitioning to a lower-carbon future, benefitting both the environment and farmers. This discussion promises to provide insightful, practical knowledge on employing machine learning for sustainability challenges in energy sector.

Generative modeling is an unsupervised learning technique in machine learning. It involves automatically discovering, and learning, the patterns in the input data. Once learns, it can be used to generate new examples that would be similar to the original dataset. Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods. The challenge (Synthetic Observability Data Generation using GANs) proposes to use GANs for generating infrastructure time-series data (data that has time dependency). Using GANs to generate datasets that should preserve temporal dynamics is challenging compared to generating Images, for example.

This talk will provide a detailed overview of GANs, covering some hands-on exercises using Tensorflow. The talk will also include explanation of GANs for time-series Data, taking TGANs as example. The talk will begin with a quick survey of existing GANs for Time-Series Data, and end with a discussion on possible GANs to consider for this challenge.